Expanding Urinary Metabolite Annotation through Integrated Mass Spectral Similarity Networking

The urine metabolome constitutes
a rich source of functional information
reflecting physiological states that are influenced by distinct conditions
and biological stresses, such as responses to drug treatments or disease
manifestations. Although global liquid chromatography–mass
spectrometry (MS) profiling provides the most comprehensive measurement
of metabolites in complex biological samples, annotation remains a
challenge, and computational approaches are necessary to translate
the molecular composition into biological knowledge. Here, we investigated
the use of tandem MS-based enhanced molecular networks (MolNetEnhancer)
to improve the metabolite annotation of urine extracts. The samples
(n = 10) were analyzed by hydrophilic interaction
chromatography–quadrupole time-of-flight mass spectrometry
in both electrospray ionization (ESI) modes. Consistent with other
common data preprocessing software, the use of Progenesis QI led to
the annotation of up to 20 metabolites based on MS2 library searches,
showing a high fragmentation score (cosine similarity ≥ 0.7),
that is, ∼2% of mass features containing MS2 spectra. Molecular
networking based on library matching resulted in the annotation of
up to 62 urinary compounds. Using a combination of unsupervised substructure
discovery (MS2LDA), the in silico tool network annotation
propagation (NAP), and ClassyFire chemical ontology, embedded in a
multilayered molecular network by MolNetEnhancer, we were able to
expand the chemical characterization to ∼50% of the data set.
The integrative approach led to the annotation of 275 compounds at
the metabolomics standards initiative (MSI) confidence level 2, as
well as 459 and 578 urinary metabolites (MSI level 3) in both negative
and positive ESI modes, respectively. The exhaustive MS2-based annotation
outperformed similar studies applied to larger cohorts while offering
the discovery of metabolites not identified by the MS2 library search.
This is the first work that effectively integrates orthogonal annotation
methods and MS2-based fragmentation studies to improve metabolite
annotation in urine samples.